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1.
J Allergy Clin Immunol ; 143(6): 2062-2074, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-30579849

RESUMO

BACKGROUND: Epigenetic mechanisms, including methylation, can contribute to childhood asthma. Identifying DNA methylation profiles in asthmatic patients can inform disease pathogenesis. OBJECTIVE: We sought to identify differential DNA methylation in newborns and children related to childhood asthma. METHODS: Within the Pregnancy And Childhood Epigenetics consortium, we performed epigenome-wide meta-analyses of school-age asthma in relation to CpG methylation (Illumina450K) in blood measured either in newborns, in prospective analyses, or cross-sectionally in school-aged children. We also identified differentially methylated regions. RESULTS: In newborns (8 cohorts, 668 cases), 9 CpGs (and 35 regions) were differentially methylated (epigenome-wide significance, false discovery rate < 0.05) in relation to asthma development. In a cross-sectional meta-analysis of asthma and methylation in children (9 cohorts, 631 cases), we identified 179 CpGs (false discovery rate < 0.05) and 36 differentially methylated regions. In replication studies of methylation in other tissues, most of the 179 CpGs discovered in blood replicated, despite smaller sample sizes, in studies of nasal respiratory epithelium or eosinophils. Pathway analyses highlighted enrichment for asthma-relevant immune processes and overlap in pathways enriched both in newborns and children. Gene expression correlated with methylation at most loci. Functional annotation supports a regulatory effect on gene expression at many asthma-associated CpGs. Several implicated genes are targets for approved or experimental drugs, including IL5RA and KCNH2. CONCLUSION: Novel loci differentially methylated in newborns represent potential biomarkers of risk of asthma by school age. Cross-sectional associations in children can reflect both risk for and effects of disease. Asthma-related differential methylation in blood in children was substantially replicated in eosinophils and respiratory epithelium.

2.
BMC Bioinformatics ; 16: 307, 2015 Sep 24.
Artigo em Inglês | MEDLINE | ID: mdl-26399714

RESUMO

BACKGROUND: Sequencing technologies provide a wealth of details in terms of genes, expression, splice variants, polymorphisms, and other features. A standard for sequencing analysis pipelines is to put genomic or transcriptomic features into a context of known functional information, but the relationships between ontology terms are often ignored. For RNA-Seq, considering genes and their genetic variants at the group level enables a convenient way to both integrate annotation data and detect small coordinated changes between experimental conditions, a known caveat of gene level analyses. RESULTS: We introduce the high throughput data integration tool, htsint, as an extension to the commonly used gene set enrichment frameworks. The central aim of htsint is to compile annotation information from one or more taxa in order to calculate functional distances among all genes in a specified gene space. Spectral clustering is then used to partition the genes, thereby generating functional modules. The gene space can range from a targeted list of genes, like a specific pathway, all the way to an ensemble of genomes. Given a collection of gene sets and a count matrix of transcriptomic features (e.g. expression, polymorphisms), the gene sets produced by htsint can be tested for 'enrichment' or conditional differences using one of a number of commonly available packages. CONCLUSION: The database and bundled tools to generate functional modules were designed with sequencing pipelines in mind, but the toolkit nature of htsint allows it to also be used in other areas of genomics. The software is freely available as a Python library through GitHub at https://github.com/ajrichards/htsint.


Assuntos
Biologia Computacional/métodos , Bases de Dados Genéticas , Genoma Humano , Genômica/métodos , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Linguagens de Programação , Software , Humanos , Análise de Sequência de RNA
3.
J Immunol Methods ; 409: 54-61, 2014 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-24727143

RESUMO

The accurate identification of rare antigen-specific cytokine positive cells from peripheral blood mononuclear cells (PBMC) after antigenic stimulation in an intracellular staining (ICS) flow cytometry assay is challenging, as cytokine positive events may be fairly diffusely distributed and lack an obvious separation from the negative population. Traditionally, the approach by flow operators has been to manually set a positivity threshold to partition events into cytokine-positive and cytokine-negative. This approach suffers from subjectivity and inconsistency across different flow operators. The use of statistical clustering methods does not remove the need to find an objective threshold between between positive and negative events since consistent identification of rare event subsets is highly challenging for automated algorithms, especially when there is distributional overlap between the positive and negative events ("smear"). We present a new approach, based on the Fß measure, that is similar to manual thresholding in providing a hard cutoff, but has the advantage of being determined objectively. The performance of this algorithm is compared with results obtained by expert visual gating. Several ICS data sets from the External Quality Assurance Program Oversight Laboratory (EQAPOL) proficiency program were used to make the comparisons. We first show that visually determined thresholds are difficult to reproduce and pose a problem when comparing results across operators or laboratories, as well as problems that occur with the use of commonly employed clustering algorithms. In contrast, a single parameterization for the Fß method performs consistently across different centers, samples, and instruments because it optimizes the precision/recall tradeoff by using both negative and positive controls.


Assuntos
Citocinas/sangue , Citometria de Fluxo/normas , Laboratórios/normas , Ensaio de Proficiência Laboratorial/normas , Leucócitos Mononucleares/imunologia , Monitorização Imunológica/normas , Algoritmos , Automação Laboratorial/normas , Biomarcadores/sangue , Fidelidade a Diretrizes/normas , Humanos , Variações Dependentes do Observador , Guias de Prática Clínica como Assunto/normas , Valor Preditivo dos Testes , Desenvolvimento de Programas , Controle de Qualidade , Indicadores de Qualidade em Assistência à Saúde/normas , Reprodutibilidade dos Testes , Manejo de Espécimes/normas
4.
BMC Syst Biol ; 6 Suppl 3: S7, 2012.
Artigo em Inglês | MEDLINE | ID: mdl-23282411

RESUMO

BACKGROUND: Contemporary high-throughput analyses often produce lengthy lists of genes or proteins. It is desirable to divide the genes into functionally coherent subsets for further investigation, by integrating heterogeneous information regarding the genes. Here we report a principled approach for managing and integrating multiple data sources within the framework of graph-spectrum analysis in order to identify coherent gene subsets. RESULTS: We investigated several approaches to integrate information derived from different sources that reflect distinct aspects of gene functional relationships including: functional annotations of genes in the form of the Gene Ontology, co-mentioning of genes in the literature, and shared transcription factor binding sites among genes. Given a list of genes, we construct a graph containing the genes in each information space; then the graphs were kernel transformed so they could be integrated; finally functionally coherent subsets were identified using a spectral clustering algorithm. In a series of simulation experiments, known functionally coherent gene sets were mixed and recovered using our approach. CONCLUSIONS: The results indicate that spectral clustering approaches are capable of recovering coherent gene modules even under noisy conditions, and that information integration serves to further enhance this capability. When applied to a real-world data set, our methods revealed biologically sensible modules, and highlighted the importance of information integration. The implementation of the statistical model is provided under the GNU general public license, as an installable Python module, at: http://code.google.com/p/spectralmix.


Assuntos
Biologia Computacional/métodos , Redes Reguladoras de Genes , Algoritmos , Animais , Pesquisa Biomédica , Análise por Conglomerados , Bases de Dados Genéticas , Expressão Gênica , Genes Fúngicos , Genoma Humano , Genômica , Humanos , Camundongos , Análise em Microsséries , Modelos Estatísticos , Saccharomyces cerevisiae/genética , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo
5.
J Comput Graph Stat ; 19(3): 552-568, 2010 Sep 01.
Artigo em Inglês | MEDLINE | ID: mdl-20877445

RESUMO

We propose a distribution-free approach to detect nonlinear relationships by reporting local correlation. The effect of our proposed method is analogous to piece-wise linear approximation although the method does not utilize any linear dependency. The proposed metric, maximum local correlation, was applied to both simulated cases and expression microarray data comparing the rd mouse with age-matched control animals. The rd mouse is an animal model (with a mutation for the gene Pde6b) for photoreceptor degeneration. Using simulated data, we show that maximum local correlation detects nonlinear association, which could not be detected using other correlation measures. In the microarray study, our proposed method detects nonlinear association between the expression levels of different genes, which could not be detected using the conventional linear methods. The simulation dataset, microarray expression data, and the Nonparametric Nonlinear Correlation (NNC) software library, implemented in Matlab, are included as part of the online supplemental materials.

6.
Bioinformatics ; 26(12): i79-87, 2010 Jun 15.
Artigo em Inglês | MEDLINE | ID: mdl-20529941

RESUMO

MOTIVATION: The results of initial analyses for many high-throughput technologies commonly take the form of gene or protein sets, and one of the ensuing tasks is to evaluate the functional coherence of these sets. The study of gene set function most commonly makes use of controlled vocabulary in the form of ontology annotations. For a given gene set, the statistical significance of observing these annotations or 'enrichment' may be tested using a number of methods. Instead of testing for significance of individual terms, this study is concerned with the task of assessing the global functional coherence of gene sets, for which novel metrics and statistical methods have been devised. RESULTS: The metrics of this study are based on the topological properties of graphs comprised of genes and their Gene Ontology annotations. A novel aspect of these methods is that both the enrichment of annotations and the relationships among annotations are considered when determining the significance of functional coherence. We applied our methods to perform analyses on an existing database and on microarray experimental results. Here, we demonstrated that our approach is highly discriminative in terms of differentiating coherent gene sets from random ones and that it provides biologically sensible evaluations in microarray analysis. We further used examples to show the utility of graph visualization as a tool for studying the functional coherence of gene sets. AVAILABILITY: The implementation is provided as a freely accessible web application at: http://projects.dbbe.musc.edu/gosteiner. Additionally, the source code written in the Python programming language, is available under the General Public License of the Free Software Foundation. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Biologia Computacional/métodos , Genes , Animais , Bases de Dados Genéticas , Perfilação da Expressão Gênica/métodos , Humanos , Software
7.
Mol Syst Biol ; 6: 349, 2010.
Artigo em Inglês | MEDLINE | ID: mdl-20160710

RESUMO

Sphingolipids including sphingosine-1-phosphate and ceramide participate in numerous cell programs through signaling mechanisms. This class of lipids has important functions in stress responses; however, determining which sphingolipid mediates specific events has remained encumbered by the numerous metabolic interconnections of sphingolipids, such that modulating a specific lipid of interest through manipulating metabolic enzymes causes 'ripple effects', which change levels of many other lipids. Here, we develop a method of integrative analysis for genomic, transcriptomic, and lipidomic data to address this previously intractable problem. This method revealed a specific signaling role for phytosphingosine-1-phosphate, a lipid with no previously defined specific function in yeast, in regulating genes required for mitochondrial respiration through the HAP complex transcription factor. This approach could be applied to extract meaningful biological information from a similar experimental design that produces multiple sets of high-throughput data.


Assuntos
Proteínas de Saccharomyces cerevisiae/metabolismo , Saccharomyces cerevisiae/enzimologia , Esfingosina/análogos & derivados , Teorema de Bayes , Análise por Conglomerados , Metabolismo dos Lipídeos , Redes e Vias Metabólicas , Mutação , Saccharomyces cerevisiae/química , Proteínas de Saccharomyces cerevisiae/genética , Transdução de Sinais , Esfingolipídeos/metabolismo , Esfingosina/genética , Esfingosina/metabolismo , Biologia de Sistemas/métodos , Fatores de Transcrição
8.
BMC Res Notes ; 2: 122, 2009 Jul 07.
Artigo em Inglês | MEDLINE | ID: mdl-19583843

RESUMO

BACKGROUND: The Gene Ontology is the most commonly used controlled vocabulary for annotating proteins. The concepts in the ontology are organized as a directed acyclic graph, in which a node corresponds to a biological concept and a directed edge denotes the parent-child semantic relationship between a pair of terms. A large number of protein annotations further create links between proteins and their functional annotations, reflecting the contemporary knowledge about proteins and their functional relationships. This leads to a complex graph consisting of interleaved biological concepts and their associated proteins. What is needed is a simple, open source library that provides tools to not only create and view the Gene Ontology graph, but to analyze and manipulate it as well. Here we describe the development and use of GOGrapher, a Python library that can be used for the creation, analysis, manipulation, and visualization of Gene Ontology related graphs. FINDINGS: An object-oriented approach was adopted to organize the hierarchy of the graphs types and associated classes. An Application Programming Interface is provided through which different types of graphs can be pragmatically created, manipulated, and visualized. GOGrapher has been successfully utilized in multiple research projects, e.g., a graph-based multi-label text classifier for protein annotation. CONCLUSION: The GOGrapher project provides a reusable programming library designed for the manipulation and analysis of Gene Ontology graphs. The library is freely available for the scientific community to use and improve.

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